A short-term predictive system for surface currents from a rapidly deployed coastal HF radar network

Abstract

In order to address the need for surface trajectory forecasts following deployment of coastal HF radar systems during emergency-response situations (e.g., search and rescue, oil spill), a short-term predictive system (STPS) based on only a few hours data background is presented. First, open-modal analysis (OMA) coefficients are fitted to 1-D surface currents from all available radar stations at each time interval. OMA has the effect of applying a spatial low-pass filter to the data, fills gaps, and can extend coverage to areas where radial vectors are available from a single radar only. Then, a set of temporal modes is fitted to the time series of OMA coefficients, typically over a short 12-h trailing period. These modes include tidal and inertial harmonics, as well as constant and linear trends. This temporal model is the STPS basis for producing up to a 12-h current vector forecast from which a trajectory forecast can be derived. We show results of this method applied to data gathered during the September 2010 rapid-response demonstration in northern Norway. Forecasted coefficients, currents, and trajectories are compared with the same measured quantities, and statistics of skill are assessed employing 16 24-h data sets. Forecasted and measured kinetic variances of the OMA coefficients typically agreed to within 10-15%. In one case where errors were larger, strong wind changes are suspected and examined as the cause. Sudden wind variability is not included properly within the STPS attack we presently employ and will be a subject for future improvement.

Evaluating the surface circulation in the Ebro delta (northeastern Spain) with quality-controlled high-frequency radar measurements

“…Other approaches are focused, in addition, on replacing noisy values with more reliable estimates (Wyatt et al, 2015) by using open-boundary model analysis (Kaplan and Lekien, 2007) or statistical mapping (Barrick et al, 2012). However, the main drawback lies with the potential removal of accurate data when the discriminating algorithm is based on tight thresholds.…”

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

“…Some recent works have applied empirical models to HFR data to obtain Short Term Predictions (STP), typically in a 24-h window. Barrick et al (2012) used OMA decomposition (Kaplan and Lekien, 2007) and then a set of temporal modes was fitted to the time series of OMA coefficients over a short training period. Frolov et al (2012) used Empirical Orthogonal Functions (EOF) decomposition of HFR fields and a vector autoregressive model on the leading EOFs time series for prediction, incorporating wind stress forecast from a regional atmospheric model.…”

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

“…To overcome these data gaps, various interpolation techniques have been applied to HF radar total vector fields. These algorithms that include 2DVAR (Yaremchuk and Sentchev 2009), normal modes (Lipphardt et al 2000), open modal analysis (Kaplan and Lekien 2007), and statistical mapping (Barrick et al 2012;O'Donnell et al 2005) have largely been applied to the UWLS surface current maps after the UWLS combination. Recently, Kim et al (2007) introduced a method that interpolates data as part of the combination step from radial component vectors to total vector maps.…”